A novel method of fingerprint segmentation based on Discrete Fourier Transform is proposed in this paper. Fingerprint segmentation is still not a fully solved problem. The proposed method utilizes the band energy in the power spectrum of DFT and detects the foreground quantitatively. Without the statistical features, this method can be applied to different types of scanners and have a robust performance. The experimental results show that this is a promising method.

In this paper, we survey various Robust Object Recognition Algorithms. One of the core technologies for local feature detector is Scale Invariant Feature Transform. And we compared several algorithms with SIFT based on IPP technology. As a result, the conversion of source codes using IPP is sped up. And this will be more improved recognition speed using SIMD Instructions.

This paper presents an adaptive image segmentation for facial photos that is independent to background colors and robust to shadows on backgrounds. Firstly, a coarse segmentation is obtained by combining the Canny edge detector and morphological operations. Then, the input image is converted into normalized RGB color model and each normalized pixel is then classified into the foreground or background. Finally, detection of face candidate regions is done using skin color information. The combination of the face candidate regions and the segmentation in normalized color model is carried out to achieve foreground segmentation without shadows.

In this paper, we present a faster more efficient object recognition method for moving robots. Recognizing places or objects in real time can be a difficult task due to many different features. To attain this task, we implemented a Parallel feature extract detector using OpenMP. In the first step, Algorithms and codes are optimized in order to execute parallel processing. The processed algorithms in parallel are debugged for robust Performance. As a result, the intelligent conversion of the source codes to exploit OpenMP technology increase processing speed by two and half times over previous detectors while still maintaining robust performance measured using a standard evaluation set

Fingerprint sample quality is one of major factors influencing the matching performance of fingerprint recognition systems. The purpose of this paper is to assess the effectiveness of individual sample quality measures on the performance of minutiae-based fingerprint recognition algorithms. Initially, the authors examined the various factors that influenced the matching performance of the minutiae-based fingerprint recognition algorithms. Then, the existing measures for fingerprint sample quality were studied and the more effective quality measures were selected and compared with two image quality software packages in terms of matching performance. The experimental results over various datasets show that even a single sample quality measure can enhance the matching performance effectively.

This paper presents a method to locate the head in facial photos based on skin color and facial features, namely mouth, chin and crown. Firstly, the image segmentation process is performed to separate the subject and the background. Then, skin pixels are extracted from the original image and the direction of the head is estimated based on the orientation of skin pixels. Finally, detections of mouth, chin and crown are done

Performance study on methodologies of quality evaluation of fingerprint

Journal

CISC-S08-한국정보보호학희 동계정보보호학술대희 논문집pp.147-152

Author

Hakil Kim

Abstract

Fingerprint sample quality evaluation (FSQE) is an important issue in fingerprint recognition. This paper reviews existing methodologies for FSQE and gives out the definition of fingerprint quality. Then, the representative set of quality measures is analyzed by studying their performance and their correlation. The purpose of this work is to produce a single scalar quantity based on human perception by combining existing quality measures. For the experiments, eleven FVC data sets were selected. In order to compare the distribution of fingerprint quality scores, all of the selected databases were combined into one database by ignoring the difference of sensor type and be tested for each quality measures.

This paper presents a fast feature extraction method for mobile robots by parallel processing based on OpenMP and SSE (Streaming SIMD Extension) programming. Recognizing scenes or objects in real-time is a difficult task, due to the variety of features. To complete this task, a parallel feature extraction detector and descriptor via OpenMP and SSE is proposed. In the first step, the algorithms and codes are optimized, in order to be implemented by parallel processing. The implemented parallel algorithms are debugged, to maintain the same level of performance. The process of extracting key points and obtaining the dominant orientation with respect to key points is parallelized via these steps. After extraction, a parallel descriptor via SSE instructions is constructed. As a result, the proposed Parallel-Hessian accelerates processing by up to two and half times over previous detectors, and the descriptor achieves an acceleration of up to four and half times compared to the original SIFT, while maintaining a robust performance measured via a standard evaluation set

Directional Fields play important roles in fingerprint recognition. A robust method of pixel-wise DFs estimation based on Gaussian filter is proposed in this paper. Firstly, the covariance data for each pixel were computed and smoothed by a Gaussian filter. Then the sine and cosine components are computed from the squared gradient vectors which were derived from covariance data, and filtered by the Gaussian filter again. Finally, the pixel-wise DFs were obtained from the inverse tangent function. The FVC databases were employed in this study and the experimental results show the proposed method can work effectively for fingerprints captured from any sensors in the FVC databases.

A singular point detection method based on high-resolution orientation field is proposed in this paper. Firstly, complex angle differential is computed from the high-resolution Orientation Field (OF), then, the singular point in OF is extracted and taken as candidate singular point. Finally, the Poincare Index (PI) is utilized to verify the candidate point. The proposed algorithm is tested on FVC2000 DB 2b, and shows robust experimental results.

This paper proposes a robust method for analyzing color facial images to extract four lip features, namely, mouth corners and the centers of inner upper/lower lips, upon a condition that the positions of two eye centers are provided in advance. Four lip features are located by taking the characteristic of lip color and edge information into account. The strong relation between positions of eyes and mouth is utilized to restrict the area where the lip features are observed. An experimental validation has been conducted on 300 images collected from the FERET database and has demonstrated the accuracy and robustness of the proposed method.

This paper presents an image alignment algorithm for application of AOI (Automatic optical inspection) based on SIFT. Since the correspondences result using SIFT descriptor have many wrong points for aligning, this paper modified and classified those points by five measures. Experimental results show that the proposed method has similar rotation and robust translation accuracy in comparison to the commercial software MIL 8.0

STATISTICAL NOISE BAND REMOVAL FOR SURFACE CLUSTERING OF HYPERSPECTRAL DATA

Journal

Proceedings of International Symposium on Remote Sensing pp 111~114

Author

Hakil Kim

Abstract

The existence of noise bands may deform the typical shape of the spectrum, making the accuracy of clustering degraded. This paper proposes a statistical approach to remove noise bands in hyperspectral data using the correlation coefficient of bands as an indicator. Considering each band as a random variable, two adjacent signal bands in hyperspectral data are highly correlative. On the contrary, existence of a noise band will produce a low correlation. For clustering, the unsupervised k-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID. Furthermore, this paper proposes a hierarchical scheme of combining those. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures. The experimental results conducted on a 228-band hyperspectral data show that while the SAM measure is rather resistant, the performance of SID measure is more sensitive to noise.

Proceedings of the 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008

Author

Hakil Kim

Abstract

This paper presents a novel method of iris location for iris recognition. In detecting the inner boundary of the iris, the method introduces a circular filter which is specially designed to detect circular shapes in iris images. The inner boundary detection process consists of two steps. Firstly, a coarse center of the pupil is detected by the proposed filter. Then, a set of inner boundary points is found by applying the linear Hough transform to rectangular areas, converted from extracted regions based on the coarse center. The inner boundary is finally determined by using the least square method to fit the set of points to a circle. The presented method further proposes a process of extracting the outer boundary of the iris by applying a linear filter to the rectangular areas transformed from arc regions around the iris. Experimental results show that the proposed method performs promisingly on iris images captured in ideal and non-ideal conditions from the CASIA iris database.

This paper proposes a novel method for fingerprint liveness detection based on band-selective Fourier spectrum. The 2D spectrum of a fingerprint image reflects the distribution and strength in spatial frequencies of ridge lines. The ridge-valley texture of the fingerprint produces a ring pattern around the center in the Fourier spectral image and a harmonic ring pattern in the subsequent ring. Both live and fake fingerprints produce these rings, but with different amplitudes in different spatial frequency bands. Typically, live fingerprints show stronger Fourier spectrum in the ring patterns than the fake. The proposed method classifies the live and the fake fingerprints by analyzing the band-selective Fourier spectral energies in the two ring patterns. The experimental results demonstrate this approach to be a promising technique for making fingerprint recognition systems more robust against fake-finger-based spoofing vulnerabilities.

This Fingerprint sample quality is one of major factors influencing the matching performance of fingerprint recognition systems. The error rates of fingerprint recognition systems can be decreased significantly by removing poor quality fingerprints. The purpose of this paper is to assess the effectiveness of individual sample quality measures on the performance of minutiae-based fingerprint recognition algorithms. Initially, the authors examined the various factors that influenced the matching performance of the minutiae-based fingerprint recognition algorithms. Then, the existing measures for fingerprint sample quality were studied and the more effective quality measures were selected and compared with two image quality software packages, (NFIQ from NIST, and QualityCheck from Aware Inc.) in terms of matching performance of a commercial fingerprint matcher (Verifinger 5.0 from Neurotechnologija). The experimental results over various Fingerprint Verification Competition (FVC) datasets show that even a single sample quality measure can enhance the matching performance effectively.

A Fast Feature Extraction in Objectr Recognition Using Parallel processing on CPU and GPU

Journal

2009 IEEE International Conference on Systems, Man, and Cybernetics

Author

Eunsoo Park, Xuenan Cui, Hakil Kim

Abstract

Due to the advents of multi-core CPU and GPU, various parallel processing techniques have been widely applied to many application fields including computer vision. This paper presents a parallel processing technique for realtime feature extraction in object recognition by autonomous mobile robots, which utilizes both CPU and GPU by combining OpenMP, SSE (Streaming SIMD Extension) and CUDA programming. Firstly, the algorithms and codes for feature extraction are optimized and implemented in parallel processing. After the parallel algorithms are assured to maintain the same level of performance, the process for extracting key points and obtaining dominant orientation with respect to the key points is parallelized. Following the extraction is the construction of a parallel descriptor via SSE instructions. Finally, the GPU version of SIFT is also implemented using CUDA. The experiments have shown that the CPU version of SIFT is almost five times faster than the original SIFT while maintaining robust performance. Further, the GPU-Parallel descriptor achieves acceleration up to five times higher than the CPU-Parallel descriptor at a cost of a bit lower performance.

IEEE Third international conference on biometrics: Theory, Application and System (BTAS 09)

Author

Hakil Kim

Abstract

This paper introduces an eye-verifier for reliable detection of eyes in facial color images. At first, the eye region candidates are searched by the circular filter to the binary face image. The eye candidates are then fed into an eye-verifier. The eye-verifier uses a ternary template, generated from the eye area consisting of iris, sclera and skin. Then the template matching is made by ternary Hamming distance. Experimental results evaluating the proposed method on our own face database and FERET database show promising performance in terms of detection rate. The proposed algorithm showed a robust performance to the face poses and eye directions

This paper presents an approach for locating eye features in color images based on the unsupervised K-means clustering. Given the assumption that the input is an eye window containing a single eye, the proposed method detects the iris by unsupervised K-means clustering on the feature spaces of compensated red and green color channels. The iris circle is then refined using the gradient information and circular Hough transform. For the sclera detection, the r-g and r-b are utilized as they show the discriminant feature of sclera regardless of light condition and iris color. The sclera is then extended to fit the eyelids by a region growing scheme. Experiments on a collection of eye images extracted from FERET facial database and our self-collected images show a promising performance toward the low illumination and iris color variety.

Noise band removal is a crucial step before spectral matching since the noise bands can distort the typical shape of spectral reflectance, leading to degradation on the matching results. This paper proposes a statistical noise band removal method for hyperspectral data using the correlation coefficient between two bands. The correlation coefficient measures the strength and direction of a linear relationship between two random variables. Considering each band of the hyperspectral data as a random variable, the correlation between two signal bands is high; existence of a noisy band will produce a low correlation due to ill-correlativeness and undirectedness. The unsupervised k-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID in order to evaluate the validation of the proposed method. This paper also proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures. The experimental results conducted on a 228-band hyperspectral data show that while the SAM measure is rather resistant, the performance of SID measure is more sensitive to noise.

Hyperspectral images have shown a great potential for the applications in resource management, agriculture, mineral exploration and environmental monitoring. However, due to the large volume of data, processing of hyperspectral images faces some difficulties. This paper introduces the development of an image processing tool (HYVIEW) that is particularly designed for handling hyperspectral image data. Current version of HYVIEW is dealing with efficient algorithms for displaying hyperspectral images, selecting bands to create color composites, and atmospheric correction. Three band-selection schemes for producing color composites are available based on three most popular indexes of OIF, SI and CI. HYVIEW can effectively demonstrate the differences in the results of the three schemes. For the atmospheric correction, HYVIEW utilizes a pre-calculated LUT by which the complex process of correcting atmospheric effects can be performed fast and efficiently.

This paper presents a fast feature extraction method for autonomous mobile robots utilizing parallel processing and based on OpenMP, SSE (Streaming SIMD Extension) and CUDA programming. In the first step on CPU version, the algorithms and codes are optimized and then implemented by parallel processing. The parallel algorithms are debugged to maintain the same level of performance and the process for extracting key points and obtaining dominant orientation with respect to key points is parallelized. After extraction, a parallel descriptor via SSE instructions is constructed. And the GPU version also implemented by parallel processing using CUDA based on the SIFT. The GPU-Parallel descriptor achieves an acceleration up to five times compared with the CPU-Parallel descriptor, but it shows the lower performance than CPU version. CPU version also speed-up the four and half times compared with the original SIFT while maintaining robust performance.

Fingerprint sample quality is one of major factors influencing the matching performance of fingerprint recognition systems. The error rates of fingerprint recognition systems can be decreased significantly by removing poor quality fingerprints. The purpose of this paper is to assess the effectiveness of individual sample quality measures on the performance of minutiae-based fingerprint recognition algorithms. Initially, the authors examined the various factors that influenced the matching performance of the minutiae-based fingerprint recognition algorithms. Then, the existing measures for fingerprint sample quality were studied and the more effective quality measures were selected and compared with two image quality software packages, (NFIQ from NIST, and QualityCheck from Aware Inc.) in terms of matching performance of a commercial fingerprint matcher (Verifinger 5.0 from Neurotechnologija). The experimental results over various Fingerprint Verification Competition (FVC) datasets show that even a single sample quality measure can enhance the matching performance effectively.

This paper presents a Gabor texture feature extraction method for classification of discolored Metal pad images using GPU(Graphics Processing Unit). The proposed algorithm extracts the texture information using Gabor filters and constructs a pattern map using the extracted information. Finally, the golden pad images are classified by utilizing the feature vectors which are extracted from the constructed pattern map. In order to evaluate the performance of the Gabor texture feature extraction algorithm based on GPU, a sequential processing and parallel processing using OpenMP in CPU of this algorithm were adopted.Also, the proposed algorithm was implemented by using Global memory and Shared memory in GPU. The experimental results were demonstrated that the method using Shared memory in GPU provides the best performance. For evaluating the effectiveness of extracted Gabor texture features, an experimental validation has been conducted on a database of 20 Metal pad images and the experiment has shown no mis-classification.

Stereo matching is the research area that regarding the estimation of the distance between objects and camera using different view points and it still needs lot of improvements in aspects of speed and accuracy. This paper presents a fast stereo matching algorithm based on plane-converging belief propagation that uses message passing convergence in hierarchical belief propagation. Also, stereo matching technique is developed using GPU and it is available for real-time applications. The error rate of proposed Plane-converging Belief Propagation algorithm is similar to the conventional Hierarchical Belief Propagation algorithm, while speed-up factor reaches 2.7 times

This paper presents a robust method for iris segmentation. To detect the inner boundary of the iris, the method introduces a circular filter, which is specially designed to detect circular shapes in iris images. The inner boundary detection process consists of two steps. Firstly, the coarse center of the pupil is detected via the proposed filter. Then, a set of inner boundary points is found by detecting segment lines using the Radon transform in the rectangular areas which are converted from extracted arc regions with the coarse center. The inner boundary is finally determined by fitting the set of points to a circle using the least square method. In addition, the proposed method includes a process for extracting the outer boundary of the iris by applying a linear filter to the rectangular areas transformed from arc regions around the iris. Furthermore, a fast method for detecting eyelids after iris segmentation is presented in this paper. Experimental results over the CASIA iris databases show that the performance of the proposed methods is promising for iris images captured in unconstrained environments.